Mapping expectancy-based appetitive placebo effects onto the brain in women

Suggestions about hunger can generate placebo effects on hunger experiences. But, the underlying neurocognitive mechanisms are unknown. Here, we show in 255 women that hunger expectancies, induced by suggestion-based placebo interventions, determine hunger sensations and economic food choices. Functional magnetic resonance imaging in a subgroup (n = 57/255) provides evidence that the strength of expecting the placebo to decrease hunger moderates medial prefrontal cortex activation at the time of food choice and attenuates ventromedial prefrontal cortex (vmPFC) responses to food value. Dorsolateral prefrontal cortex activation linked to interference resolution formally mediates the suggestion-based placebo effects on hunger. A drift-diffusion model characterizes this effect by showing that the hunger suggestions bias participants’ food choices and how much they weigh tastiness against the healthiness of food, which further moderates vmPFC–dlPFC psychophysiological interactions when participants expect decreased hunger. Thus, suggestion-induced beliefs about hunger shape hunger addressing economic choices through cognitive regulation of value computation within the prefrontal cortex.


Replication of the suggestion-based placebo intervention on hunger ratings in the fMRI sample
As shown in Supplementary Figure 1 below, placebo effects on hunger ratings were replicated in the smaller fMRI sample.In more detail, for the behavioral pilots, we observed a significantly greater increase in hunger from baseline to the end of the experiment for the increased-(mean hunger increasebaseline to end = 0.96, sem = 1.16) than the decreased-hunger (mean hunger decreasebaseline to end = 0.22, sem = 0.14) suggestion group (t(113) = 3.49, p < 0.001, Cohen's d = 0.61).We observed a similar difference for the smaller fMRI sample: increased-hunger suggestion group (mean hunger increase baseline to end = 0.62, sem = 0.27), in which the participants reported being significantly hungrier at the end of the experiment than at baseline and than participants of the decreased-hunger (mean hunger decreasebaseline to end = 1.35, sem = 0.22) suggestion group (t(55) = 2.11, p = 0.04, Cohen's d = 0.56).The suggestion effect was not significantly different in magnitude between the pilot and fMRI samples, as indicated by a non-significant hunger suggestion groupincreased vs decreased by testing timebaseline vs end of experiment by sample behavioral pilots vs fMRI interaction: F(2,343) = 0.4, p = 0.67).
We therefore pooled the two groups for comparisons of the decreased-and increased-hunger suggestion groups for behavioral and computational variables.

2.1.
Relationship between the food stimulus value and calorie content of the food Overall, we found that the participants did not assign significantly greater stimulus values to high-calorie food than low-calorie food, although there was a slight tendency (t(254) = -1.92,p = 0.06, paired-sample two-tailed t-test).We obtained the same result when comparing the stimulus values for low-calorie vs high-calorie food for all three groups: decreased-hunger suggestion group (t(87)D = -0.39, p = 0.70, paired-sample two-tailed t-test), increasedhunger suggestion group (t(83)I = -1.60,p = 0.11, paired-sample two-tailed t-test), and control group (t(82)C = -1.46, p = 0.15, paired-sample two-tailed t-test).
The overall stronger correlation between stimulus value and high caloric food compared to the correlation between stimulus value and low-calorie food reported in the main text was driven by the increased-hunger suggestion group.Participants in this group showed a stronger correlation with high-(average Pearson's R = 0.21) than low-(average Pearson's R = -0.13)calorie foods (z = -2.16,p = 0.03, two-tailed r-to-z transformation).A similar pattern was observed for the control group (average Pearson's Rhigh = 0.15, Rlow = -0.13,z = -1.8,p = 0.07, two-tailed r-to-z transformation) versus the decreased-hunger suggestion group (average Pearson's Rhigh = 0.14, Rlow = -0.11,z = -1.62,p = 0.11, two-tailed r-to-z transformation.The differences between groups for the SV correlation were not significant for low-calorie or high-calorie food items.However, LME analysis (

Correlation of tastiness ratings with caloric density
To assess whether the subjective taste ratings correlated with the calorie content of the food items, the calorie density (i.e., calories per gram) was correlated for each participant to the participant's tastiness ratings.Individual Pearson's correlation coefficients were then compared between the increased-and decreased-hunger suggestion groups, and the control group.Tastiness and calorie density did not correlate in the whole sample (t(254) = -0.38,p = 0.71, one-sample t-test), neither in the decreased-(t(87)D = -1.57,p = 0.12), control-(t(82)C = 1.65, p = 0.10) nor increased-(t(83)I = -0.44,p = 0.66, one-sample t-test) hunger suggestion groups.Furthermore, there was no difference in the correlation coefficient of tastiness to calorie density between the groups, the LME analysis did not reveal a group effect (βgroup = 0.01 ± 0.01, t(253) = 0.87, p = 0.39, 95% CI [-0.02 -0.04]), and post-hoc t-tests comparing the groups 2-by-2 did not show any significant difference.This finding indicates that the participants of our sample did not consider high-calorie food to be tastier.

Frequency of response categories during dietary decision-making
For each food, participants had to rate how much they wanted to eat the food at the end of the experiment using a 4-point Likert scale with four possible response types: strong no, no, yes, and strong yes.The occurrence of each response type (strong no, no, yes, strong yes) was calculated as the average percentage for a specific type of response: A two-factor LME analysis (group coded -1, 0, 1 and response type coded -2, -1, 0, revealed a main effect of response type (βresponse type = -  S2).On the contrary, the increased-hunger suggestion group, as well as the control group, responded significantly more often with a "strong yes" than the decreased-hunger suggestion group (increased vs decreased: t (170) =

Responses by tastiness and healthiness categories
Dietary decision-making trials were split into four categories based on the participants' tastiness and healthiness ratings: Healthy-Tasty, Healthy-Untasty, Unhealthy-Tasty, and Unhealthy-Untasty food choices.
To compare the three groups, we used an LME with the percentage of yes responses as the dependent variable and group (-1, 0, 1) and food category (1 for healthy and tasty, 2 for healthy but untasty, 3 for unhealthy but tasty, 4 for unhealthy and untasty) as fixed effects and found a main effect of category (βfood category = -10.83± 0.80, t(1014) = -13.61,p < 0.001, 95% CI [-12.39 --9.27]) but no effect of group nor an interaction between group and food category, indicating that all participants were more willing to consume tastier food items, regardless of group (Figure S3).However, participants in the increased-hunger suggestion group accepted healthy-tasty food more often (t(170) = -2.79,p = 0.006, Cohen's d = -0.43,two-sample two-tailed t-test) and rejected unhealthy-tasty food less often (t(170) = -3.34,p = 0.001, Cohen's d = -0.51,two-sample two-tailed t-test) than participants in the decreasedhunger suggestion group (Figure S3).The control group also chose healthy-tasty foods significantly more frequently than the decreased hunger group (t(169) = 3.24, p = 0.001,

Regulatory success
The regulatory success score (RSS) during dietary decision-making was defined as the sum of accepted healthy-untasty food (yes & strong yes responses) and rejected unhealthy-tasty food (no & strong no responses) over all healthy-untasty and unhealthy-tasty food items using the formula: RSS = Σ Yes healthy, untasty + No unhealthy, tasty Σ healthy, untasty food + unhealthy, tasty food A linear mixed-effects model was fitted to the regulatory success score with group as a predictor variable and BMI added as a regressor.The outcomes highlight a main effect of group (βgroup = -0.05± 0.01, t(252) = -3.62,p < 0.001, 95% CI [-0.08 --0.02]) and no effect of BMI.
Participants in the decreased-hunger suggestion group showed a greater RSS (mean RSSD = 0.38 ± 0.02) than participants in the increased-hunger suggestion group (mean RSSI = 0.26 ± 0.02) and those in the control group (mean RSSC = 0.31 ± 0.02).Once again, the increased hunger group and the control group did not show any significant difference in the regulatory success score.

Healthy eating index scores (HEI-2015)
A 24-hour recall questionnaire ascertained the complete dietary intake of the previous 24 hours for the control participants and those who underwent fMRI.This questionnaire involved obtaining a list of all food items eaten on the previous day by a trained dietitian (the 2 nd author of the study).The interviewer probed for frequently forgotten foods, such as savory snacks, sugary beverages, candies, etc.Then, the time and name of the eating occasion (i.e., breakfast, lunch, dinner) and a detailed description of the type of food item, cooking method, and other relevant aspects were collected.
This information was then used to calculate the energy intake and the healthy eating index Each component weights differently in the final scoring, which ranges between 0-100, where higher scores indicate a healthier dietary pattern.The components and their respective weights were: total fruits (5 points), whole fruits (5 points), total vegetables (5 points), greens and beans (5 points), whole grains (10 points), dairy (10 points), total protein foods (5 points), fatty acids (polyunsaturated fatty acid plus the monounsaturated fatty acid to saturated fatty acid ratio) (10 points), refined grains (10 points), sodium (10 points), added sugars (10 points), and saturated fats (10 points).Refined grains, sodium, added sugars, and saturated fats, as components to be consumed in moderation, were reversely scored 1 .A recall was also conducted one month after the experiment and the results were essentially the same, which indicates that this index is a stable measure of everyday diet across a period of one month.
As shown in SI table 16 HEI-2015 scores indicated that eating was comparable and not different between the two hunger-suggestion groups and the control group, and eating patterns were still at similar levels one month after the experiment for the two suggestion groups.Although this evidence provides an indirect indication of the status of the energy stores on the day of the experiment, it provides some evidence that the eating habits of the participants did not differ the day before the experiment.

Brain activation in correlation with tastiness and healthiness
To localize where in the brain the tastiness and healthiness attributes were encoded and whether such encoding differed between the two hunger-suggestion groups, we fitted two general linear models (GLMs) to BOLD timeseries with the following regressors: an onset regressor at the time of choice (duration: reaction time) parametrically moderated by tastiness (GLM1) or healthiness (GLM2).Both GLMs also included onset regressors for missed trials (boxcar durations of 3s) and the six realignment parameters as regressors of non-interest to correct for head movements.Individual beta estimates for each regressor were then fitted into a second-level random effects analysis using one-sample t-tests for the entire sample (N=57) and two-sample t-tests to compare the decreased-(N=28) to increased-(N=29) hunger suggestion group.
Tastiness at the time of food choice activated the ventromedial prefrontal cortex (vmPFC), posterior cingulate cortex (PCC), precuneus, frontal eye fields, and dorsolateral and dorsomedial prefrontal cortices (pFWE = 0.05, family-wise error corrected for the whole brain based on peak height; Figure S6a, SI Table 10).No differences were observed between groups, even with more lenient uncorrected thresholds (p < 0.001).Healthiness at the time of food choice was encoded in the bilateral amygdala (pFWE = 0.05, family-wise error corrected based on peak height, Figure S6b, SI Table 11).At a lower uncorrected threshold, additional activation was found in the medial prefrontal cortex (p < 0.001 uncorrected; Figure S6b, SI Table 5).No differences in brain responses to healthiness at time of food choice were found between hunger-suggestion groups.Interestingly, conjunction maps showed the mPFC to correlate with both attributes, but positively with tastiness and negatively with healthiness.This finding suggests that this region integrates the weights of these two attributes differently.

Variables on a trial-by-trial level
The priors for predicted choice and reaction time data (ycpt) were drawn from each trial from a univariate Wiener distribution using the formula: ycpt ~ dwieners(ɑt = 2 , Ƭcpt, βcpt, δcpt , ɑcp) Priors for hidden latent variables on the trial-by-trial level were defined by: • Non-decision time variable Ƭcpt = cp.

Parameter recovery
To determine if parameters obtained from the winning 2 weights DDM were identifiable, a parameter recovery was conducted.
To this aim new data was simulated for choices and reaction times using the DDM model and the RWiener package with the optimal parameter values and observed tastiness and healthiness ratings for the 255 participants.The total trial numbers were determined by each participant's number of observed food choices.The simulated data was then fitted by the 2 weights DDM using the same estimation method in JAGS as for the generating parameters, which gave a new set of recovered parameters.Both sets of parameters were then compared using Pearson's correlations.Parameter recovery was good (Figure S7 and SI Table 19).The generating and recovered parameters correlated significantly for the parameters of interest (SI Table 19).

Brain mediators of placebo effects on hunger experiences
The serial path regressions for the dlPFC mediation model showed a significant path a effect (beta = 0.49, se = 0.22, p = 0.02), which reflected the univariate effect of the hungersuggestion group (increased > decreased hunger suggestion) on dlPFC activation at the time of food choice.After controlling for the path a effect, dlPFC activation significantly predicted the change in hunger from baseline to the end of the experiment (path b: beta = 0.32, se = 0.11, p < 0.01).Finally, mediation of the suggestion on hunger was significant (path a*b beta = 0.15, se = 0.09, p < 0.01), with a non-significant total effect of hunger suggestion on the hunger ratings after controlling for the mediator (path c': beta = 0.21, se =0.17, p = 0.26).
These findings suggest that the group difference between increased-and decreased-hunger suggestions on hunger ratings can be formally explained by the activation of interferenceresolution regions located in the dlPFC at the time of making the food choice.

Multi-Source-Interference task (MSIT)
To localize brain activation related to cognitive regulation, such as attentional filtering of task-relevant information, the participants of the fMRI experiment performed two sessions of the multi-source interference task (MSIT; SI Figure 8).The task design followed a previously reported procedure [2][3][4] .Briefly, the goal of the task was to select the digit 1, 2, or 3 as quickly and accurately as possible by pressing the corresponding response button (i.e., index finger for '1', middle finger for '2', and ring finger for '3').During congruent control trials, the target number matched its position within zero distractor digits on the computer screen (e.g., 1 0 0 or 0 2 0 or 0 0 3).On the contrary, during incongruent interference trials, the target digit never matched its position within the non-zero distractor digits (e.g., 2 1 2).
This condition created a Stroop effect and required allocation of attentional resources to inhibit task-irrelevant information and filter task-relevant information to provide a correct response.Before performing this task within the fMRI scanner, all participants trained for a full session outside the scanner.

Behavioral Results
Four participants were excluded from the behavioral analyses because they misunderstood the task instructions.These four participants reported the place of the target number for the incongruent trials.For example, they pressed the third response button for a "1 1 2" trial, whereas the correct answer was pressing the second button.Two of them performed the training task perfectly but failed during the MRI session despite the instructions being repeated before each session.For the other two participants, performance during the incongruent trials was already close to zero during the training session.For these two participants, it appears that the instructions were not well understood and that the experimenter remained unaware of this during the training session.

MSIT Brain Imaging Results
A multilevel general linear model contained two onset regressors for the congruent and incongruent trial blocks and six realignment parameters as covariates of non-interest to control for head movement.Individual beta estimates for the contrast between task condition (congruent versus incongruent) were then fitted into a second-level random effects analysis using one-sample t-tests to localize brain responses associated with interference resolution (congruent < incongruent) and the opposite effect (congruent > incongruent).
Whole brain activation for interference resolution (incongruent > congruent) involved the dorsal anterior cingulate cortex (dACC), dorsolateral prefrontal cortex (dlPFC), parietal cortex, and anterior insula, which were more highly activated in the incongruent than congruent trials (pFWE < 0.05, family-wise error corrected based on peak height, Figure S8, SI Table 21).In contrast, stronger activation of the brain's reward and valuation system was found in the congruent than incongruent trials.This brain activation involved the vmPFC, posterior cingulate cortex, ventral striatum, hippocampus, and posterior insula (pFWE < 0.05, family-wise error corrected based on peak height, Figure S8, SI Table 22).No significant differences were found for the congruent versus incongruent contrast between the hunger suggestion groups.

Details of the placebo intervention
Information about the drink given to the participants assigned to the decreased-hunger suggestion group: "Before starting the computer/fMRI task, we will ask you to drink a glass of this refreshing, non-carbonated, and neutral-flavored drink, which will help you to focus better on the exercises that you will perform later on the computer/during fMRI.This drink has exceptional nutritional benefits, such as the presence of amino acids, carbohydrates, and traces of elements that are essential for health and brain functioning.Specifically, it has been enriched with riboflavin, also known as vitamin B2, which acts positively on leptin secretion.Leptin is a natural hormone produced by your body that helps to burn fat and reduce food cravings.One glass of this drink is an excellent source of chromium, a formidable anti-sugar element that acts on the metabolism of carbohydrates.It is an insulin cofactor that promotes glucose absorption.Chromium deficiency can cause sugar cravings, with signs of hypoglycemia.Moreover, this water contains vitamins B6, B12, and E, stimulating the development of neural tissue and cell formation, which also act positively on the activity of the neurotransmitter serotonin, known as a well-being hormone involved in mood and concentration.In addition, this water is enriched with mineral salts, such as magnesium and iron, which are essential in case of anemia and a valuable ally for women's health because they provide oxygen to the cells and reduce the feeling of fatigue.This drink is an excellent source of extra food during sports and intellectual endeavors.A 25-cl glass covers all the nutritional needs of the next two hours.This drink is natural, gluten-free, and packaged in a protected environment.You will have five minutes to drink a glass of the water.Thereafter, you should wait a few minutes to allow time for the active substance to deliver their effect.We will then ask you to perform a food-choice task." Information about the drink provided to participants in the increased-hunger suggestion group: "Before starting the experiment, we will ask you to drink a glass of this refreshing, noncarbonated, and neutral-flavored drink, which will help you to focus better on the exercises that you will perform later on the computer/during fMRI.This drink has exceptional nutritional benefits, such as the presence of amino acids, carbohydrates, and traces of elements that are essential for health and brain functioning.Specifically, it has been enriched with zinc, which promotes digestion, fights against heartburn, and stimulates the taste buds.It also brings together the benefits of medicinal plants such as St. John's wort, known for its stimulating effect on hunger.Its action is based on its ability to send a hunger message to the brain.Several studies have shown that this medicinal plant acts positively on the activity of the neurotransmitter serotonin, known as a well-being hormone involved in mood and concentration.In addition, this water is also enriched with mineral salts, such as magnesium and iron, essential in cases of anemia and a valuable ally for women's health because they provide oxygen to the cells and reduce the feeling of fatigue.This drink is natural, gluten-free, and packaged in a protected environment.You will have five minutes to drink a glass of the water.Thereafter, you should wait a few minutes to allow time for the active substance to deliver their effect.We will then ask you to perform a food-choice task.

Figure S1 .
Figure S1.Placebo effects on hunger ratings for the pilot and fMRI samples.Raincloud plots for hunger ratings from baseline to the end of the experiment for the pilot behavioral sample (left side) and the fMRI sample (right side).Each dot corresponds to the difference in a participant's hunger rating from baseline to the end of the experiment, with positive values indicating an increase in hunger ratings.Distributions of the increase in hunger ratings are shown to the right of each plot.Boxplots show the 95% CI for both hunger-suggestion groups and samples.The black and gray lines in each boxplot indicate the median and the whiskers range from minimum and maximum values within 1.5 times the interquartile range.*p < 0.05.p-values were obtained with two-sampled, two-tailed t-tests.Source data are provided as a Source Data file.

Figure S2 .
Figure S2.Average percentage of each type of response during the dietary decision-making task for the decreased-(pink bars, N = 88), increased-(dark blue bars, N = 84) hunger suggestion groups, and control group (gray bars, N = 83).Boxplots show 95% CI with black lines indicating the median, and whiskers range from minimum to maximum values within 1.5 times the interquartile range.Black dots indicate outlier values.**P < 0.01, *P < 0.05.P-values were obtained with two-sample, two-tailed ttests.Source data and exact p-values are provided as a Source Data file.

Figure S3 .
Figure S3.Average percentage of binned food acceptance (yes + strong yes responses) as a function of the healthiness and tastiness categories of food in each hunger suggestion group (N = 88 for decreased, N = 84 for increased, and N = 83 for control group).The boxplots show 95% CI with black and gray lines indicating the median, and whiskers range from minimum to maximum values within 1.5 times the interquartile range.Dots indicate individual participant values.***P < 0.001, **P < 0.01.P-values were obtained by two-sample, two-tailed t-tests.Source data and exact p values are provided as a Source Data file.

Figure S4 .
Figure S4.Boxplots displaying the 95% CI for the regulatory success score according to the suggestion group (decreased hunger (pink, N = 88) or increased hunger (dark blue, N = 84), and the control group (gray, N = 83)), with jitter elements showing dots for the individual score of each participant.The black and gray vertical lines indicate the median and the whiskers range from minimum to maximum values

(
HEI-2015) from the day before for each participant.The HEI-2015 scores were computed based on the Dietary Guidelines for Americans 2015-2020 (DGA, and following Krebs-Smith et al. 2018 1 ).These guidelines consist of 13 components scored following adequacy-or moderation-based energy-adjusted intake cut-offs, (except for the fatty-acid components).

Figure S5 .
Figure S5.Brain activation at the time of food choice in response to (a) the tastiness and (b) healthiness of food.Statistical parametric maps (SPMs) for tastiness are displayed at pFWE < 0.05 corrected for multiple comparisons using family-wise error whole brain corrected the on voxel level.The SPMs for healthiness are displayed at p < 0.001 uncorrected with an extent threshold of k = 45 for healthiness.Voxels in yellow and red are superimposed on the average anatomical image.(c) Conjunction map of tastiness (yellow) and healthiness (red) SPMs displayed at p < 0.001, uncorrected.(d) SPMs for common regions activated in response to tastiness and healthiness displayed at p < 0.001, uncorrected.

Figure S6 .
Figure S6.Scheme of the hierarchical Bayesian tDDM that was fitted to the observed choices and reaction times.The blue shapes denote the observed variables that varied on a trial-by-trial basis: the evidence, Ecpt (defined by equations 6, main text), and choice and reaction time data, ycpt (see below).Hidden latent variables are shown as clear shapes at every level of the hierarchy.

Figure S7 .
Figure S7.Parameter recovery for N = 255 participants.The histograms show the distribution of (a) the starting point bias, (b) the taste and (c) health drift weights for N = 255 participants.Panels below show the correlations (Pearson correlation coefficient) between the observed (generating) and recovered parameters for (d) the starting point bias, (e) taste and (f) health drift weight.The red dotted line indicates the x = y identity line.Source data are provided as a Source Data file.

Figure S8 .
Figure S8.Brain activation during the MSIT task for interference resolution (congruent < incongruent trials) and control (congruent > incongruent trials).SPMs are displayed at pFWE < 0.05, family-wise error whole brain corrected based on peak height.Significant voxels in red and yellow are superimposed on the average anatomical image.

Table 4 .
SI Table1.Positive moderation of brain activation by prognostic expectations at the time of food choice in the decreased-hunger suggestion group, N =28 IpS -intraparietal sulcus; AG -angular gyrus; mPFC -medial prefrontal cortex; dPCC -dorsal posterior cingulate cortex.Values are reported at p < 0.001, uncorrected, using an extend threshold of k = 50.*Regionsthatsurvivedfamily-wiseerrorcorrectionbased on the cluster level.SI Table2.Firstlevel results from a multilevel general linear model of stimulus value measured in the decreased hunger suggestion participants (N=88).Firstlevel results from a multilevel general linear model of stimulus value measured in the increased hunger suggestion participants (N=84).TR -Taste Ratings; HR -Health Ratings.Source data are provided as a Source Data file.SI Table7.Second level, random effects results using weighted two-sample, two-tailed ttests to compare individual beta estimates from the first level between increased-hunger TR -Taste Ratings; HR -Health Ratings; STE -Standard Error.Source data are provided as a Source Data file.TR -Taste Ratings; HR -Health Ratings.Source data are provided as a Source Data file.

Table 9 .
1ifferential brain activation in response to the stimulus value at the time of food choice for the contrast increased > decreased hunger suggestion, N = 57.SI Table10.Positive effect of tastiness at the time of food choice for all 57 participants.SI Table11.Negative effect of healthiness at the time of food choice for N = 57 participants.SI Table12.Average beta estimates extracted at the time of food choice from regions activated during interference resolution trials of the MSIT on N = 57 participants.betaestimateswerefirstextractedfromROIsatthetime of choice during the dietary decision-making task.Error values correspond to the standard error of the mean.dACC-dorsalanteriorcingulatecortex;dlPFC-dorsolateralprefrontalcortex.Source data are provided as a Source Data file.-posteriordistributions of each parameter for decreased and increased suggestion groups.PPposterior probability that differences are > zero.w-driftweights,-non-decisiontime,BIASinitialstartingpointbias.Note, the * indicates that for the healthiness drift weight the average PDs and the PP were calculated assuming (ddecreased -dincreased > 0).SI Table14.Whole brain activation for the psychophysiological interaction with the vmPFC as a seed ROI (N = 57).Note, education was coded as 1 for high school undergraduate, 2 for high school graduate, 3 for two years of higher education (college), and 4 for more than two years of college.SCOFF, YFAS, and IPAQ average scores were calculated for the fMRI study participants only (N = 57).SCOFF -Sick, Control, One stone, Fat, Food; BDI -Beck's Depression Inventory, YFAS -Yale Food Addiction Scale, IPAQ-sf -International Physical Activity Questionnaire short form.Source data are provided as a Source Data file.SI table 16.HEI scores for N = 57 participants of the fMRI experiment and N=54 participants of the control study.eatingindexesscoreswerecalculatedfollowingKrebs-Smithetal.(2018)1.A score of 100 indicates total correspondence of eating behavior with the 2015-2020 Dietary Guidelines for Americans.No differences between groups on the HEI-2015 scores were observed for eating on the day before the fMRI, and one month later.SEM -standard error of the mean.Source data are provided as a Source Data file.SI table17.Food stimuli used during the dietary decision-making task for the behavioral pilot experiment (n=200 trials) and the fMRI experiment (n=152 trials) -seed for the hyper-condition parameter mean  -seed for the hyper-condition parameter precision or error term bias.kappa-seedweight of the shape and rate of the gamma prior distribution for the participant level bias parameter SI Table19.Parameter recovery.Pearson's correlation coefficients between generating and recovered parameters ΔPerformance -difference in performance between congruent trials and incongruent trials; RTreaction time ; ΔRT -difference in reaction times between congruent trials and incongruent trials.Source data are provided as a Source Data file.